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1. Introduction

In this quick tutorial, we’ll demonstrate how to merge two maps using the Java 8 capabilities.

To be more specific, we’ll examine different merging scenarios, including maps that have duplicate entries.

2. Initialization

To start, we’ll define two Map instances:

private static Map<String, Employee> map1 = new HashMap<>();
private static Map<String, Employee> map2 = new HashMap<>();

The Employee class looks like this:

public class Employee {
 
    private Long id;
    private String name;
 
    // constructor, getters, setters
}

Then we can push some data into the Map instances:

Employee employee1 = new Employee(1L, "Henry");
map1.put(employee1.getName(), employee1);
Employee employee2 = new Employee(22L, "Annie");
map1.put(employee2.getName(), employee2);
Employee employee3 = new Employee(8L, "John");
map1.put(employee3.getName(), employee3);

Employee employee4 = new Employee(2L, "George");
map2.put(employee4.getName(), employee4);
Employee employee5 = new Employee(3L, "Henry");
map2.put(employee5.getName(), employee5);

Note that we have identical keys for the employee1 and employee5 entries in our maps, which we’ll use later.

3. Map.merge()

Java 8 adds a new merge() function into the java.util.Map interface.

The merge() function works as follows; if the specified key isn’t already associated with a value, or the value is null, it associates the key with the given value.

Otherwise, it replaces the value with the results of the given remapping function. If the result of the remapping function is null, it removes the result.

First, we’ll construct a new HashMap by copying all the entries from the map1:

Map<String, Employee> map3 = new HashMap<>(map1);

Next, we’ll introduce the merge() function, along with a merging rule:

map3.merge(key, value, (v1, v2) -> new Employee(v1.getId(),v2.getName())

Finally, we’ll iterate over the map2 and merge the entries into map3:

map2.forEach(
  (key, value) -> map3.merge(key, value, (v1, v2) -> new Employee(v1.getId(),v2.getName())));

Let’s run the program and print the content of map3:

John=Employee{id=8, name='John'}
Annie=Employee{id=22, name='Annie'}
George=Employee{id=2, name='George'}
Henry=Employee{id=1, name='Henry'}

As a result, our combined Map has all the elements of the previous HashMap entries. Entries with duplicate keys have been merged into one entry.

Also, we can see that the Employee object of the last entry has the id from map1, and the value is picked from map2.

This is because of the rule we defined in our merger function:

(v1, v2) -> new Employee(v1.getId(), v2.getName())

4. Stream.concat()

The Stream API in Java 8 can also provide an easy solution to our problem. First, we need to combine our Map instances into one Stream. This is exactly what the Stream.concat() operation does:

Stream combined = Stream.concat(map1.entrySet().stream(), map2.entrySet().stream());

Here we passed the map entry sets as parameters.

Next, we need to collect our result into a new Map. For that, we can use Collectors.toMap():

Map<String, Employee> result = combined.collect(
  Collectors.toMap(Map.Entry::getKey, Map.Entry::getValue));

As a result, the collector will use the existing keys and values of our maps. But this solution is far from being perfect. As soon as our collector meets entries with duplicate keys, it’ll throw an IllegalStateException.

To handle this issue, we can simply add a third “merger” lambda parameter into our collector:

(value1, value2) -> new Employee(value2.getId(), value1.getName())

It’ll use the lambda expression every time a duplicate key is detected.

Finally, we’ll put it all together:

Map<String, Employee> result = Stream.concat(map1.entrySet().stream(), map2.entrySet().stream())
  .collect(Collectors.toMap(
    Map.Entry::getKey, 
    Map.Entry::getValue,
    (value1, value2) -> new Employee(value2.getId(), value1.getName())));

Now let’s run the code and see the results:

George=Employee{id=2, name='George'}
John=Employee{id=8, name='John'}
Annie=Employee{id=22, name='Annie'}
Henry=Employee{id=3, name='Henry'}

As we can see, the duplicate entries with the key “Henry” were merged into a new key-value pair, where the id of the new Employee was picked from map2 and the value from map1.

5. Stream.of()

To continue to use the Stream API, we can turn our Map instances into a unified stream with the help of Stream.of().

Here we don’t have to create an additional collection to work with the streams:

Map<String, Employee> map3 = Stream.of(map1, map2)
  .flatMap(map -> map.entrySet().stream())
  .collect(Collectors.toMap(
    Map.Entry::getKey,
    Map.Entry::getValue,
    (v1, v2) -> new Employee(v1.getId(), v2.getName())));

First, we transform map1 and map2 into a single stream. Next, we convert the stream into the map. As we can see, the last argument of toMap() is a merging function. It solves the duplicate keys problem by picking the id field from v1 entry, and the name from v2.

Here’s the printed map3 instance after running the program:

George=Employee{id=2, name='George'}
John=Employee{id=8, name='John'}
Annie=Employee{id=22, name='Annie'}
Henry=Employee{id=1, name='Henry'}

6. Simple Streaming

Additionally, we can use a stream() pipeline to assemble our map entries. The code snippet below demonstrates how to add the entries from map2 and map1 by ignoring the duplicate entries:

Map<String, Employee> map3 = map2.entrySet()
  .stream()
  .collect(Collectors.toMap(
    Map.Entry::getKey,
    Map.Entry::getValue,
    (v1, v2) -> new Employee(v1.getId(), v2.getName()),
  () -> new HashMap<>(map1)));

As we expect, the results after the merge are:

{John=Employee{id=8, name='John'}, 
Annie=Employee{id=22, name='Annie'}, 
George=Employee{id=2, name='George'}, 
Henry=Employee{id=1, name='Henry'}}

7. StreamEx

In addition to solutions provided by the JDK, we can also use the popular StreamEx library.

Simply put, StreamEx is an enhancement for the Stream API, and provides many additional useful methods. We’ll use an EntryStream instance to operate on key-value pairs:

Map<String, Employee> map3 = EntryStream.of(map1)
  .append(EntryStream.of(map2))
  .toMap((e1, e2) -> e1);

The idea is to merge the streams of our maps into one. Then we’ll collect the entries into the new map3 instance. It’s also important to mention the (e1, e2) -> e1 expression, as it helps to define the rule for dealing with the duplicate keys. Without it, our code will throw an IllegalStateException.

And now, the results:

{George=Employee{id=2, name='George'}, 
John=Employee{id=8, name='John'}, 
Annie=Employee{id=22, name='Annie'}, 
Henry=Employee{id=1, name='Henry'}}

8. Summary

In this brief article, we learned different ways of merging maps in Java 8. More specifically, we used Map.merge(), Stream API, and the StreamEx library.

The code backing this article is available on GitHub. Once you're logged in as a Baeldung Pro Member, start learning and coding on the project.
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Baeldung Pro comes with both absolutely No-Ads as well as finally with Dark Mode, for a clean learning experience:

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Partner – Microsoft – NPI EA (cat = Spring Boot)
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Azure Container Apps is a fully managed serverless container service that enables you to build and deploy modern, cloud-native Java applications and microservices at scale. It offers a simplified developer experience while providing the flexibility and portability of containers.

Of course, Azure Container Apps has really solid support for our ecosystem, from a number of build options, managed Java components, native metrics, dynamic logger, and quite a bit more.

To learn more about Java features on Azure Container Apps, visit the documentation page.

You can also ask questions and leave feedback on the Azure Container Apps GitHub page.

Partner – Orkes – NPI EA (cat = Spring)
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Modern software architecture is often broken. Slow delivery leads to missed opportunities, innovation is stalled due to architectural complexities, and engineering resources are exceedingly expensive.

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With Orkes Conductor managed through Orkes Cloud, developers can focus on building mission critical applications without worrying about infrastructure maintenance to meet goals and, simply put, taking new products live faster and reducing total cost of ownership.

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Partner – Orkes – NPI EA (tag = Microservices)
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Modern software architecture is often broken. Slow delivery leads to missed opportunities, innovation is stalled due to architectural complexities, and engineering resources are exceedingly expensive.

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With Orkes Conductor managed through Orkes Cloud, developers can focus on building mission critical applications without worrying about infrastructure maintenance to meet goals and, simply put, taking new products live faster and reducing total cost of ownership.

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